Design and Evaluation of New Intelligent Sensor Placement Algorithm to Improve Coverage Problem in Wireless Sensor Networks

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Given a finite number of sensors, optimizing the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. In many working environments, for achieving good coverage, we must able to placement of sensors in adequate places. In this article we apply the simulated annealing, genetic and learning automata as intelligent methods for solving the sensor placement in distributed sensor networks. In the distributed sensor networks, the sensor placement is a NP-complete problem for arbitrary sensor fields and it is one of the most important issues in the research fields, so the proposed algorithm is going to solve this problem by considering two factors: one is the complete coverage and the second one is the minimum cost. The proposed method is examined in different areas by C language. The results not only confirm the successes of using new method in sensor placement, also they show that the new method is more efficiently in large areas compared to other methods like PBIL.